AWS Certified Generative AI Developer - Professional (AIP-C01) Practice Test

This certification is designed for software and AI developers who build and deploy generative AI solutions on AWS or with open-source tooling. The target candidate typically holds a role such as AI/ML developer, cloud developer, or solutions engineer and is responsible for integrating foundation models into business applications, constructing agentic workflows, and ensuring those solutions are secure, cost-effective, and production-ready.

AWS recommends candidates have at least two years of experience building production-grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience, and a minimum of one year of hands-on experience implementing generative AI solutions. Professionals transitioning into AI-focused development roles from software engineering or data engineering backgrounds are also well-positioned to pursue this certification.

There are no mandatory prerequisite certifications for the AIP-C01 exam. However, AWS recommends that candidates consider earning the AWS Certified AI Practitioner, AWS Certified Solutions Architect – Associate, AWS Certified Machine Learning Engineer – Associate, or AWS Certified Data Engineer – Associate before attempting this Professional-level exam, as those credentials build foundational knowledge that is assumed in the AIP-C01 content.

Candidates should bring working knowledge of AWS compute, storage, and networking services; AWS security best practices and identity and access management; deployment and infrastructure-as-code tools (e.g., AWS CloudFormation, AWS CDK); monitoring and observability services (e.g., Amazon CloudWatch); and AWS cost optimization principles. Familiarity with core GenAI concepts—foundation models, embeddings, vector databases, prompt engineering, and RAG architectures—is essential before attempting the exam.

The AIP-C01 exam consists of 75 total questions: 65 scored questions and 10 unscored pretest questions that are indistinguishable during the exam and do not affect the final score. AWS uses the unscored questions to evaluate them for future inclusion as scored items. The exam must be completed within 170 minutes and can be taken at a Pearson VUE testing center or via online proctored delivery. The exam is available in English and Japanese during the beta phase.

Question types include multiple choice (one correct answer out of four), multiple response (two or more correct answers that must all be selected to receive credit), ordering (arranging three to five steps in the correct sequence), and matching (correctly pairing three to seven prompt-response combinations). Scoring is compensatory—no per-domain passing threshold is required—and unanswered questions are scored as incorrect with no additional penalty for guessing. Results are reported as a scaled score from 100 to 1,000, with a minimum passing score of 750. The exam cost is $150 USD.

• 1.Domain 1: Foundation Model Integration, Data Management, and Compliance (31%) — Covers designing and implementing solutions using vector stores, RAG pipelines, knowledge bases, and GenAI architectures. Includes selecting and evaluating foundation models, managing data ingestion and chunking strategies, and ensuring data compliance and governance throughout the FM integration lifecycle.
• 2.Domain 2: Implementation and Integration (26%) — Focuses on integrating foundation models into applications and business workflows, applying advanced prompt engineering and prompt management techniques, and building agentic AI solutions including multi-agent orchestration, tool use, and action groups using services like Amazon Bedrock Agents.
• 3.Domain 3: AI Safety, Security, and Governance (20%) — Assesses the ability to implement security controls, access management, and guardrails for GenAI applications; enforce responsible AI principles; detect and mitigate risks such as prompt injection, hallucination, and bias; and apply compliance and governance frameworks using AWS native controls.
• 4.Domain 4: Operational Efficiency and Optimization for GenAI Applications (12%) — Covers optimizing GenAI workloads for cost, latency, and throughput; selecting appropriate inference options (on-demand, provisioned, batch); applying caching strategies; and measuring and improving business value of deployed GenAI solutions.
• 5.Domain 5: Testing, Validation, and Troubleshooting (11%) — Evaluates the ability to monitor GenAI applications using Amazon CloudWatch and AWS-native observability tools, design evaluation frameworks for FM output quality, troubleshoot common GenAI failure modes (e.g., hallucinations, retrieval failures, context window issues), and iteratively optimize solution performance.

• Start with the official AIP-C01 Exam Guide available at docs.aws.amazon.com and the structured Exam Prep Plan on AWS Skill Builder (skillbuilder.aws), which provides curated learning paths, hands-on labs, and official practice question sets mapped to each domain.
• Build hands-on experience with Amazon Bedrock—the primary AWS service tested—by practicing RAG implementations using Bedrock Knowledge Bases, creating agents with Bedrock Agents, and experimenting with Bedrock Guardrails for safety and compliance scenarios.
• Focus significant study time on Domain 1 (31%) and Domain 2 (26%), which together comprise over half the exam. Prioritize understanding embedding models, vector store selection (e.g., Amazon OpenSearch Serverless, pgvector on Aurora), chunking strategies, and prompt engineering patterns like chain-of-thought and few-shot prompting.
• Practice ordering and matching question types, which are unique to this exam compared to Associate-level AWS exams. These question types test procedural knowledge—such as the correct sequence for deploying a RAG pipeline or configuring an agent—so study workflows and architecture patterns step-by-step.
• Review AWS whitepapers and documentation on Responsible AI and the AWS Shared Responsibility Model as applied to AI/ML workloads, since Domain 3 (AI Safety, Security, and Governance) requires understanding AWS-specific controls like IAM policies for Bedrock, VPC endpoints, and encryption options for GenAI data flows.
• Use the AWS Skill Builder Official Practice Exam for AIP-C01 to simulate the real test environment. Review each incorrect answer against the official exam guide task statements, not just the explanation provided, to identify knowledge gaps by domain.
• Since this is a beta exam (available through March 31, 2026), leverage community resources such as the AWS re:Post forum, the AWS Certification LinkedIn group, and Tutorials Dojo's AIP-C01 study path for supplemental practice questions and peer insights as official third-party material is still maturing.

Holding the AWS Certified Generative AI Developer – Professional credential positions engineers for high-demand roles such as AI/ML developer, generative AI engineer, cloud application developer with AI specialization, and solutions architect focused on AI workloads. As organizations shift toward embedding AI capabilities into existing products rather than building standalone AI teams, developers who can demonstrate validated, production-grade GenAI integration skills on AWS gain a measurable competitive advantage in hiring and internal advancement. The Professional-level designation signals seniority beyond the AI Practitioner or Associate-tier credentials and aligns with engineering roles that carry greater autonomy and compensation.

The timing of this certification—launched in late 2025 alongside rapid enterprise adoption of foundation model APIs—reflects direct market demand. Professionals with proven GenAI deployment skills, particularly on the AWS ecosystem where Amazon Bedrock has become a leading enterprise FM platform, are well-positioned for salary premiums observed across cloud AI specializations. The Early Adopter badge awarded to the first 5,000 exam passers also provides an additional differentiator for early credential holders on professional profiles.